Machine learning is increasingly playing a larger and more important role in developing or improving the understanding of complex systems. As machine learning techniques have matured, machine learning has rapidly moved from the theoretical to the practical. Combined with the advent of big-data technology, machine learning solutions are being applied to a variety of industries and applications that until now were difficult, if not impossible to effectively reason about. As such, there has been an explosion of the development of different types of machine learning models that may be used predicting outcomes for different system. In some cases, some organizations may expend significant resources to develop or train machine learning models directed to different question spaces. Also, since training and tuning machine learning models may be difficult or time consuming, other organizations may be interested in using machine learning models that have been trained and tuned by other organizations. However, using public or shared machine learning models may be difficult for organizations that have secret or private information they are interested in classifying using other organizations' machine learning models. For example, undesirable sharing of private or confidential information with the owner of the shared machine learning models may be required. Likewise, other organizations that own trained models may be discouraged from sharing their trained models with others. For example, developing, training, or tuning machine learning models may be expensive or proprietary. Thus, in this example, simply providing a tuned and trained model to another organization may be disadvantageous since some or all of the internal details developed through training or tuning may be discernable by others when using it.
Accordingly, practical sharing, or the like, of machine learning models may be difficult and impractical. Thus, it is with respect to these considerations and others that the invention has been made.